Automated classification of subsurface impact damage in thermoplastic composites using depth-resolved terahertz imaging and deep learning

Reliable detection of barely visible impact damage is critical to ensure the structural integrity of composite components in service, particularly in safety-critical applications such as pressure vessels and transportation systems. This study presents a solution for detecting such damage in woven glass fiber-reinforced thermoplastic composites using terahertz (THz) time-of-flight tomography and convolutional neural networks. THz provides non-contact, non-ionizing, high-axial-resolution imaging of subsurface and back-surface damage, addressing key limitations of surface-based inspection methods. While THz imaging alone may not always permit conclusive damage identification, we bridge this gap by training neural network classifiers on depth-resolved THz B-scan images using ground truth from co-located X-ray micro-computed tomography. Among several pretrained architectures tested via transfer learning, DenseNet-121 exhibits the highest accuracy. The model remains robust even when trained on truncated B-scans excluding surface indentation features, confirming its ability to detect structural anomalies located internally or on the back surface. This is particularly relevant for applications where back-side access is not feasible. Experimental validation is performed on impacted glass-fiber-reinforced thermoplastic coupons prepared in accordance with ASTM D7136, with damage severity quantified through force–displacement data and micro-tomographic analysis. Labeling for supervised learning conforms to acceptance criteria from industrial standards for composite pressure vessels (ASME BPVC Section X, CGA C-6.2), ensuring regulatory alignment and enabling deployment in quality control workflows. The proposed method minimizes the need for expert interpretation or secondary validation and offers direct applicability to in-service inspection and manufacturing quality control

Simulation of shockless spalling fragmentation using the Discrete Element Method (DEM)

In the present study a Discrete Element Method (DEM) is considered to model the dynamic behaviour and fragmentation mechanisms of alumina ceramic under high strain-rate shockless loading. GEPI (high-pulsed power) spalling experiments are simulated. The DEM allows to take into account the accurate propagation and interaction of stress waves within the samples upon calibration of microscopic bond parameters. The results indicate that a standard failure criterion can effectively represent the spalling phenomenon, though discrepancies with experimental data increase at higher strain rates. To address this, the study combines the DEM approach with a damage law, specifically the first and second order Kachanov damage law, to model crack initiation and propagation. Comparative analysis with experimental rear face velocity profiles validates the approach. The strain-rate sensitivity of the present DEM model is explored using loading pulses of increasing intensity that induce different strain-rate levels. This research demonstrates that the DEM approach can effectively model dynamic behaviour in brittle solids leading to a multiple fragmentation sensitive to the strain rate

Why Artificial Intelligence Challenges the Foundations of Technology Acceptance Models

Despite decades of refinement, technology acceptance models such as the Technology Acceptance Model (TAM; Davis, 1985, 1989) and the Unified Theory of Acceptance and Use of Technology (UTAUT; Venkatesh et al., 2003) remain the dominant frameworks for evaluating digital technologies. Their resilience reflects robustness and parsimony. Yet Artificial Intelli-gence (AI) changes the game. Unlike earlier systems, AI learns, adapts and acts, increasingly participating in the decisions, challenging the very assumptions on which TAM/UTAUT rest. As Venkatesh himself admitted, the acceptance of AI tools remains “a question mark”, raising doubts on the adequacy of established models (Venkatesh, 2022). Drawing on a semi-systematic literature review (12,048 publications from 1985–2025, including 155 focused on AI ac-ceptance), we show that while TAM/UTAUT still account for nearly 70% of studies, the field has entered a phase of conceptual displacement. Three converging dynamics stand out: an affec-tive and experiential turn, a vulnerability-centered perspective and a socio-technical orientation. Together, they crystallize into three new research streams: trust-centered, adoption-oriented and ethics-centered, that shift the field away from individual-utilitarian framings toward relational, organizational and governance logics. The challenge ahead is clear: to decide whether constructs such as trust, affect, privacy, ethics and anthropomorphism are merely contextual moderators or the building blocks of a new paradigm. The age of AI calls for more than incremental refine-ments, it demands a shared theoretical framework capable of steering organizations and societies through both the promises and risks of intelligent systems

Automated classification of subsurface impact damage in thermoplastic composites using depth-resolved terahertz imaging and deep learning

Reliable detection of barely visible impact damage is critical to ensure the structural integrity of composite components in service, particularly in safety-critical applications such as pressure vessels and transportation systems. This study presents a solution for detecting such damage in woven glass fiber-reinforced thermoplastic composites using terahertz (THz) time-of-flight tomography and convolutional neural networks. THz provides non-contact, non-ionizing, high-axial-resolution imaging of subsurface and back-surface damage, addressing key limitations of surface-based inspection methods. While THz imaging alone may not always permit conclusive damage identification, we bridge this gap by training neural network classifiers on depth-resolved THz B-scan images using ground truth from co-located X-ray micro-computed tomography. Among several pretrained architectures tested via transfer learning, DenseNet-121 exhibits the highest accuracy. The model remains robust even when trained on truncated B-scans excluding surface indentation features, confirming its ability to detect structural anomalies located internally or on the back surface. This is particularly relevant for applications where back-side access is not feasible. Experimental validation is performed on impacted glass-fiber-reinforced thermoplastic coupons prepared in accordance with ASTM D7136, with damage severity quantified through force–displacement data and micro-tomographic analysis. Labeling for supervised learning conforms to acceptance criteria from industrial standards for composite pressure vessels (ASME BPVC Section X, CGA C-6.2), ensuring regulatory alignment and enabling deployment in quality control workflows. The proposed method minimizes the need for expert interpretation or secondary validation and offers direct applicability to in-service inspection and manufacturing quality control

Investigation of non-Schmid effects in dual-phase steels using a dislocation density-based crystal plasticity model

Non-Schmid (NS) effects in body-centered cubic (BCC) single-phase metals have received special attention in recent years. However, a deep understanding of these effects in the BCC phase of dual-phase (DP) steels has not yet been reached. This study explores the NS effects in ferrite-martensite DP steels, where the ferrite phase has a BCC crystallographic structure and exhibits NS effects. The influences of NS stress components on the mechanical response of DP steels are studied, including stress/strain partitioning, plastic flow, and yield surface. To this end, the mechanical behavior of the two phases is described by dislocation density-based crystal plasticity constitutive models, with the NS effect only incorporated into the ferrite phase modeling. The NS stress contribution is revealed for two types of microstructures commonly observed in DP steels: equiaxed phases with random grain orientations, and elongated phases with preferred grain orientations. Our results show that, in the case of a microstructure with equiaxed phases, the normal NS stress components play significant roles in tension-compression asymmetry. By contrast, in microstructures with elongated phases, a combined influence of crystallographic texture and NS effect is evident. These findings advance our knowledge of the intricate interplay between microstructural features and NS effects and help to elucidate the mechanisms underlying anisotropic-asymmetric plastic behavior of DP steels

Impact of cognitive Effort, Social Interaction, Enjoyment of Learning, and Immersive Presence on Academic Achievement with Virtual Reality

Immersive technologies represent significant advancements that allow users to engage in interactive and captivating environments, both perceptually and sensorily. This study aims to enrich the understanding of the relationship between several key variables and the achievement of academic objectives when using VR. An experiment was conducted with first-year university institute of technology students who participated in a virtual visit to a biology laboratory. The primary objective is to evaluate how each of the studied variables influences academic goals. By providing insights into the key factors that determine academic success in immersive environments, this research aims to optimize the use of these technologies in educational contexts, thereby enhancing students' learning outcomes

Recent advances in the remelting process for recycling aluminium alloy chips: a critical review

This critical review examines advances in preprocessing and remelting processes for aluminium alloy chip recycling, emphasizing pre-treatment and remelting techniques that improve both resource recovery and material quality. Pre-treatment strategies, particularly cleaning methods and compaction are critically evaluated. Various cleaning methods, including centrifugation, ultrasonic solvent washing, extraction, and distillation are compared based on their ability to remove residual cutting fluids. Cold compaction, which augments chip density to approximately 2.5 g/cm³, significantly curtails oxidation losses and enhances metal recovery. During remelting, NaCl-KCl-based fluxes with limited fluoride additions (e.g., 3–7 wt% Na₃AlF₆) disrupt oxide networks but require careful dosage control to minimize furnace corrosion and environmental hazards. Moreover, mechanical stirring combined with suitable melting temperatures reduces porosity while enhancing melt purity. Future research should prioritize the development of low-energy cleaning methods, flux composition optimization, and scalable production techniques to further advance sustainable aluminium recycling

Investigation of a constitutive law for the prediction of the mechanical behavior of WEEE recycled polymer blends

This research focuses on a mechanical study of an acrylonitrile–butadiene–styrene (ABS)/ polycarbonate (PC) blend totally derived from Waste Electrical and Electronic Equipment (WEEE) recycling. First, an experimental work was developed in laboratory for the preparation of different mixtures of ABS/PC blend. Then, mechanical tensile tests were performed on the injected specimens and the stress/strain experimental data were gathered to be used in the modelling part. In order to enable the prediction of the mechanical response of the blend, G’Sell and Jonas constitutive law was considered for this purpose. An optimization method based on the Generalized Reduced Gradient (GRG) nonlinear algorithm was developed to identify the input parameters governing the mechanical model. In addition, an uncertainty parametric study was assessed to qualitatively and quantitatively evaluate the constitutive law sensitivity versus the parameter uncertainty. Monte Carlo simulations were performed and the convergence of the numerical model was proved in terms of means and standard deviation statistical data. The results showed an excellent agreement between the numerical approach and the experiments. Besides, it was highlighted the crucial role of coupling uncertainty parametric study with modelling for accurately describing the mechanical behavior of the blend

Experimental and modeling approach for estimating the psychological adaptation and perceived thermal comfort of occupants in indoor spaces

This study proposes a methodology for examining the relationship between environmental thermal conditions and occupant's perceived thermal comfort evaluation. Therefore, their psychological adaptation was examined to quantify and incorporate it in thermal comfort evaluations. To achieve the closure of the model's system of equations, experiments are carried out in which subjects are exposed to various thermal conditions in an enclosed space that simulates an office indoor environment; thermal measurements and perceived data are collected. Thus, the study aims to evaluate the adaptive factor that causes the difference between the physiological evaluation and the subjects’ actual thermal perception. This adaptive factor is linked to the physical stimuli experienced owing to the thermal environment and the cognitive information within the occupant's memory systems; thus, the closure equation is derived from the outdoor air temperature and indoor operative temperature

On the strain energy decomposition in phase field brittle fracture: established models and novel cleavage plane-based techniques

Thèse financée par ALM (Angers Loire Métropole) et ANR RockStorHy.This work offers a detailed examination of the phase field approach for modeling brittle fracture, emphasizing its theoretical foundations, mathematical descriptions, and computational strategies. Central to our discussion is an in-depth analysis of strain energy decomposition methods integral to phase field models. We introduce an innovative technique using a cleavage plane based degradation that has shown promising results under various loading scenarios. We meticulously evaluate each method’s inherent limitations and challenges to highlight their respective advantages and drawbacks across different loading scenarios. This review aims not only to catalog existing knowledge but also to pave the way for future research directions in the application of phase field approach to fracture analysis